CENTRE FOR MENTAL HEALTH RESEARCHResearch School of Population Health
College of Medicine, Biology and Environment
Prof. Luis Salvador-Carulla , MD, PhD
Centre for Mental Health Research [email protected]
CMHR
POPULATION HEALTH: -Adoption of the complexity and systems thinking approach - The shift from EBM to Evidence informed policy: context and environmental factors, prior expert knowledge and experiential knowledge- WHO Strategy: People-centred integrated care with a focus on EQUITY and EFFICIENCY (waste reduction)
HEALTH CARE SYSTEMS IN CRISIS (major challenges for MH)-Increasing costs, market inefficiencies, impact of IT, lack of relevant information for evidence-informed planning- New payment mechanisms to replace ABF and fee-for-service: bundle payments, population based payment (capitation)- New organisational approaches: Patient medical homes, accountable care organisations, recovery-new alliances private/public, health/social-New models of care
NEW DEMANDS ON ACADEMIA: IMPLEMENTATION RESEARCH-Managerial epidemiology, Impact analysis, Context analysis, Spatial analysis- Move from classical EBM/Qualitative research: Research in local areas, big data analysis, cross design synthesis, modelling and Knowledge Discovery from Data- Collaborative research: multidisciplinary teams with extensive partnership with public health agencies, providers, stakeholders and health care companies
Arthur C EvansCommissioner of Philadelphia’s Department of BehavioralHealth & ID (DBHIDS)
Michael MarmottPresident World Medical Association Director Institute of Health Equity
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Scientific Discovery
ScientificCorroboration
Implementation
• Innovation
• Hypothesis testing
• Expt. approach
• Replication of previous studies
• Scoping a systematic reviews
• Metanalysis
• Translation to the real world
• Local context
• Expert knowledge
• Rapid Synthesis, Impact analysis
The Journey to Implementation Sciences
Evidence based care(EBC)
Evidence InformedPolicy (1)
Knowledge GuidedPolicy(2)
• RCT
• Expt. approach
• EBC
• Local information / Context analysis
• Epidemiology, Routine & Big data
• Expert knowledge: Practical / Implicit
• Knowledge Discovery from Data
• Complex decision support systems
Paradigm shift: Systems thinking in Policy Decision Making
1. Lavis et al, Health Research Policy and Systems 2009 (SUPPORT MODEL )2. Gibert et al, Health Research Policy and Systems 2010 (EbCA MODEL)
Context Analysis
Evidence
Context Implementation
Expert Knowledge
Journey of MH Reform:• Is the Australian Health System efficient?• Is there enough awareness on its current problems?• Are there guiding drivers for its reform?
MH is more complex and vulnerable that other health areasMH is already under the major reform in three decades (social and health care)MH would be more affected than other health care areas
We cannot make a journey if do not know where we are: MAPPING & ACCOUNTABILITY…
There are no failures if there is monitoring: by doing and
knowing we increase organisational learning …..SYSTEMS THINKING…
Moving from pendulum to balance strategies … FOR BASIC UNIVERSAL PROVISION OF BALANCED CARE
PHN Grant Programme Guidelines v1.2 2016• Description of service availability, gap analysis, and an action plan to
address these gaps where needed
• “National Health Services Directory (NHSD)” A consistent directory of key primary health services.... capability to view health needs, overlaid with the location of the health services identified from the NHSD; and PHN websites with centralised content and “reporting dashboard”
NEW TOOLS FOR MONITORING AND DECISION MAKING
Kitchin et al. Knowing & Governing Cities through UrbanIndicators . Regional studies, regional science, 2: 6-28, 2015
SYSTEMS THINKING IS AIMED AT IMPROVING DECISION
MAKING IN A COMPLEX ENVIRONMENT BY INCREASING THE
KNOWLEDGE-BASE AND REDUCING UNCERTAINTY
It does not provide simple-single solutions but it contributes
to organisational learning
System thinking perspective for health care planning
• HEALTH SYSTEMS are dynamic social organizations of people, institutions and resources that deliver health care to meet the health needs of target populations mainly by providing health interventions.
• SYSTEM THINKING: provides a means of analysing organisations as a integrated, complex composition of many interconnected agents (human and non-human) that need to work together for the whole to function successfully
• DYNAMIC SYSTEMS can be described in terms of their goals, their components , their connections and interactions; and their functions are characterised by
• HIGH Variability• HIGH Uncertainty• HIGH Ambiguity• DIFFERENT Levels of Organisation: Simple / Complicated / Complex
• Non-linear, self-adapted, interdependent, context-dependent, time-dependent
DECISION SUPPORT SYSTEMS (DSS)
Computer systems that improve, complete, and refine the suggestions of the decision maker and send them back for validation in an iterative process to
support the solution
Intelligent DSS: support experts (do not replace them)Machine learning: representation of the input data and generalization of the learnt patterns for use on future unseen data Deep learning: automated extraction of complex data representations (features) at different levels of abstraction
SYSTEMS THINKING vs LINEAR HEALTH CARE PLANNING: MIND THE GAP!
BASQUE COUNTRY (SPAIN) / SCOTLAND NHS (UK) ALBERTA /ENGLAND (CQC)
Complex health systems: COMPONENTS
AgentsConsumersPofessionals, Teams, Organisations
ConnectionsNetworksinteractions
EnvironmentSystems, subsystems, nested systemsBoundaries and Population determinants
Non-linear, self-adapted, interconnected, context-dependent, time-dependent
Frameworks& DriversValues, goals,targets
DSS
System thinking in MH Planning
Local Atlas of care
Pathways -InterventionsIndicators
Modeling-VisualisationFinancing
Logic model/Cptual mapSocial Network Analysis
Big data
SPATIAL &
EFFICIENCY ANALYSIS
KnowledgeGuided Policy
New visualisation tools for analysis of KPIs
Relative Technical
EfficiencyPR1-PR3
PR4+PR6notnew
PR4+PR6new
PR5+PR7
Sectorization
PR8+PR11
PRother
TO5 TO6 TO7.1
PD1+PD41
PD2+PD3
PD4other
UR2stay
UR2readmissions
Modeling Community MH Care
Relative Efficiency & Benchmarking
ORTUELLAZALLA
GALDAKAO BASAURI
SESTAODURANGO
OTXARKOAGAREKALDE SANTURTZI
BERMEO AJURIAGUERRA
ETXANIZGERNIKA
BARAKALDO URIBE
ERCILLA ERANDIO
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ÁREAS DE SALUD MENTAL
ANÁLISIS DE LA EFICIENCIA TÉCNICA RELATIVA ORIENTADO A INPUTS
ESCENARIO 1: ATENCIÓN HOSPITALARIA DE AGUDOS (R2)
VALOR MEDIO DEL ESCENARIO
INPUTS
- DISPOSITIVOS POR 100.000 HABITANTES
- PLAZAS POR 100.000 HABITANTES
- PSIQUIATRAS POR 100.000 HABITANTES
- PSICÓLOGOS+DUE POR 100.000 HABITANTES
- TOTAL DE PROFESIONALES POR 100.000 HABITANTES
OUTPUTS
- ALTAS POR 1.000 HABITANTES
- DÍAS DE ESTANCIA POR INGRESO
- REINGRESOS EN MENOS DE 30 DÍAS POR 100 ALTAS
ORIENTADO A INPUTS: MINIMIZACIÓN DE
INPUTS PRODUCIENDO LOS MISMOS OUTPUTS
SocialNetworkAnalysis
Day Programs
Hospital Residential CareOutpatient Care
Community Residential Care
A SYSTEM GAP ANALYSIS DOES NOT IMPLIES SOLUTIONS: IT ONLY GENERATES NEW GUIDED QUESTIONS:
What is the impact of NOT having day care services on the local MH system’s efficiency?
?
….. AND ADDS KNOWLEDGE ON THE LOCAL SYSTEM FOR DECISION MAKING
- WESTERN SYDNEY: Problem in the structural organisation of service availability
- FAR WEST (rural): Problem in the workforce capacity of the local MH system
STOOL MODEL COMMUNITY MENTAL HEALTH CARE
I-CARE
NEW LINKS BETWEEN ORGANISATIONAL/BUSINESS RESEARCH AND HEALTH CARE RESEARCH
Do you know how good you are?
Do you know where you stand relative to the best?
Do you know where the variation exists?
Do you know the rate of improvement over time?
Four Questions of leadership, quality and efficiency
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